Geminos CauseWay
Scope, build, analyze, test and deploy data driven causal AI apps that support better decisions. Move beyond prediction to understand what happened, why it happened and what to do about it.
Cause, not correlation
Where most analytics tells you what correlates, CauseWay models cause and effect, so you can estimate the impact of an intervention, separate genuine impact from coincidence and reason about what would happen if you acted differently.
That distinction is what makes decisions defensible. A correlation can tell you that lower demand seemed to coincide with a specific program. CauseWay can tell if one or more events are actually related, how interventions affect the outcome, by how much, for whom and at what cost.
Capabilities
Build and validate causal models
Build models with your subject matter experts in a collaborative environment, or let our causal co-pilot build or refine the models for you. Drag and drop modeling and validation make causal AI practical for teams, not just specialists.
Causal co-pilot
The co-pilot works alongside you to identify new causal variables related to the model you are building. It can use public LLMs, private LLMs or KnowledgeWay, so it fits your security and sovereignty requirements.
Intervention sandbox with counterfactual reasoning
Explore the impact of change before you act. Test single or multiple interventions and reason about counterfactuals, so you can see what to do when conditions change.
Root cause analysis without the guesswork
Using time series data and attribution scores, CauseWay shows what actually drove an outcome, replacing guesswork with causal evidence.
Decision chains and causal twin modeling
Model cause and effect across connected processes, so you can quantify knock on impacts and test what to change before you act.
Connect your data easily
Plug CauseWay into your data lake and existing pipelines. It works with the ETL tools you already use and supports all major data sources.
How causal models get built: a blended approach
CauseWay supports more than one path to a validated causal model, and most projects blend them.
- Grounded in the knowledge graph. Where a KnowledgeWay Enterprise Knowledge Graph exists, the causal co-pilot draws on connected entities, relationships and evidence to propose and refine causal structure.
- Driven by subject matter expertise. Where the graph is less developed, models are built directly from expert knowledge captured in workshops using the collaborative, drag and drop environment.
- A blend of both. Most real projects lean on the graph where it is strong and on experts where it is not, then validate the result either way.
This flexibility means CauseWay delivers value whether or not a mature knowledge graph is already in place, and it strengthens as the graph grows.
Use the right intelligence for the problem
CauseWay gives teams a structured way to analyze root causes, interventions and trade offs. It can operate as a standalone product or alongside KnowledgeWay within Geminos ArchWay, our Build It Yourself architecture for teams creating and managing their own enterprise AI solutions.
Prefer Geminos to build it?
Organizations that prefer Geminos to build and support the complete application can choose a purpose built Geminos PathWay solution.
Ready to see CauseWay in action?
Bring one outcome you want to understand and we will show you what its causal drivers look like.